Chapter 3 - Data visualisation

Importing the necessary libraries

library(tidyverse)

First look at the dataset

mpg

## # A tibble: 234 × 11
##    manufacturer      model displ  year   cyl      trans   drv   cty   hwy
##           <chr>      <chr> <dbl> <int> <int>      <chr> <chr> <int> <int>
## 1          audi         a4   1.8  1999     4   auto(l5)     f    18    29
## 2          audi         a4   1.8  1999     4 manual(m5)     f    21    29
## 3          audi         a4   2.0  2008     4 manual(m6)     f    20    31
## 4          audi         a4   2.0  2008     4   auto(av)     f    21    30
## 5          audi         a4   2.8  1999     6   auto(l5)     f    16    26
## 6          audi         a4   2.8  1999     6 manual(m5)     f    18    26
## 7          audi         a4   3.1  2008     6   auto(av)     f    18    27
## 8          audi a4 quattro   1.8  1999     4 manual(m5)     4    18    26
## 9          audi a4 quattro   1.8  1999     4   auto(l5)     4    16    25
## 10         audi a4 quattro   2.0  2008     4 manual(m6)     4    20    28
## # ... with 224 more rows, and 2 more variables: fl <chr>, class <chr>

A car’s fuel efficiency vs. engine size

We want to see the relationship between a car’s engine size (displ) and it’s fuel efficiency on the highway (hwy). A car with a low fuel efficiency consumes more fuel than a car with a high fuel efficiency when they travel the same distance.

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy))

In this plot, we can see a downward trend that explains a negative relationship between the two variables, so our hypothesis is that cars with bigger engines are less fuel efficient.

3.2.4 - Exercises

1. Run ggplot(data = mpg) what do you see?

ggplot(data = mpg)

2. How many rows are in mtcars? How many columns?

nrow(mtcars)

## [1] 32

ncol(mtcars)

## [1] 11

3. What does the drv variable describe? Read the help for ?mpg to find out.

• drv describes the transmission system of the vehicle. The options are f = front-wheel drive, r = rear wheel drive, 4 = 4wd

4. Make a scatterplot of hwy vs cyl.

• hwy is the highway miles per gallon
• cyl is the number of cylinders

ggplot(data = mpg) + geom_point(mapping = aes(x = cyl, y =hwy))

5. What happens if you make a scatterplot of class vs drv. - class is the ‘type’ of car

ggplot(data = mpg) + geom_point(mapping = aes(x = class, y =drv))

6. Why is the plot not useful?

• The ‘type’ of car (e.g. minivan, 2-seater) does not determine the transmission system of the vehicle (e.g. front-wheel drive).

Incorporating another variable in the plot with color/size/alpha/shape

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, size = class))

## Warning: Using size for a discrete variable is not advised.

## alpha controls the transparency of the points
ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, shape = class))

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

3.3.1 - Exercises

1. What’s gone wrong with this code? Why are the points not blue?

• The color needs to be defined outside of aes()

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

• 6 variables have class ‘character’ - they are categorical.

summary(mpg)

##  manufacturer          model               displ            year     
##  Length:234         Length:234         Min.   :1.600   Min.   :1999  
##  Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999  
##  Mode  :character   Mode  :character   Median :3.300   Median :2004  
##                                        Mean   :3.472   Mean   :2004  
##                                        3rd Qu.:4.600   3rd Qu.:2008  
##                                        Max.   :7.000   Max.   :2008  
##       cyl           trans               drv                 cty       
##  Min.   :4.000   Length:234         Length:234         Min.   : 9.00  
##  1st Qu.:4.000   Class :character   Class :character   1st Qu.:14.00  
##  Median :6.000   Mode  :character   Mode  :character   Median :17.00  
##  Mean   :5.889                                         Mean   :16.86  
##  3rd Qu.:8.000                                         3rd Qu.:19.00  
##  Max.   :8.000                                         Max.   :35.00  
##       hwy             fl               class          
##  Min.   :12.00   Length:234         Length:234        
##  1st Qu.:18.00   Class :character   Class :character  
##  Median :24.00   Mode  :character   Mode  :character  
##  Mean   :23.44                                        
##  3rd Qu.:27.00                                        
##  Max.   :44.00

3. Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = cyl))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = cyl))

## shape cannot be applied to continuos variable

4. What happens if you map the same variable to multiple aesthetics?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = cyl, color = cyl))

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

# For shapes that have a border (like 21), you can colour the inside and
# outside separately. Use the stroke aesthetic to modify the width of the
# border 
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class, stroke = 3))

## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (geom_point).

6. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = displ < 5))

Facets

Facets are particularly useful for categorical variables. Split your plot into facets, subplots that each display one subset of the data. To facet your plot by a single variable, use facet_wrap().

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

To facet your plot on the combination of two variables, add facet_grid() to your plot call.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ class)

3.5.1 Exercises

1. What happens if you facet on a continuous variable?

• The continuous values are discretized (binned) and then plotted.

2. What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

• The empty plots mean that there are no points in our dataset that have that combination of drv and cyl values. For example, there are no 4-wheel drive cars in our dataset with 5 cylinders.

3. What plots does the following code make? What does . do?

• The . allows us to not facet in the rows or column dimension in the grid.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

4. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

• Faceting allows us to easily see the clusters formed by different classes, e.g. compact cars have high fuel efficiency.
• The disadvantage is that one plot is produced for each label so many plots need to be analysed and it’s difficult to compare values across plots.
• For larger datasets, it will be easier to make initial hypothesis using facet grids - it will be difficult to see the clusters with colours. Colours make it easier to compare since the values appear on the same figure.

5. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol variables?

• facet_wrap wraps a 1d sequence of panels into 2d. nrow and ncol represents the number of rows and columns.
• facet_grid forms a matrix of panels defined by row and column facetting variables.
• facet_grid does not have nrow and ncol because those values are obtained automatically from the levels of the variables.

6. When using facet_grid() you should usually put the variable with more unique levels in the columns. Why?

• This will put more columns and make the plot wider and this makes more sense with widescreen monitors - more viewing space.

3.6 Geometric objects

A geom is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. As we see above, you can use different geoms to plot the same data.

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))

## `geom_smooth()` using method = 'loess'

To display multiple geoms in the same plot, add multiple geom functions to ggplot():

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess'

This is a cleaner way of doing the above with less duplication:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

## `geom_smooth()` using method = 'loess'

3.6.1 Exercises

1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

## line chart - two variables: continuous x, continuous x
ggplot(data = mpg) + geom_line(mapping = aes(x = displ, y = hwy))

## box plot - two variables: discrete x, continuous x
ggplot(data = mpg) + geom_boxplot(mapping = aes(x = class, y = hwy))

  ## histogram - one variable: continuous
ggplot(data = mpg) + geom_histogram(mapping = aes(x = hwy), bins = 20)

## area chart - one variable: continuous
ggplot(data = mpg) + geom_area(stat = "bin", mapping = aes(x = hwy), bins = 20)

2. Run this code:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point(show.legend = FALSE) + 
  geom_smooth(se = FALSE, show.legend = FALSE)

## `geom_smooth()` using method = 'loess'

3. What does show.legend = FALSE do?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point(show.legend = FALSE) + 
  geom_smooth(se = FALSE, show.legend = FALSE)

## `geom_smooth()` using method = 'loess'

4. What does the se argument to geom_smooth() do?

• When se is set to TRUE, the smoothed out line shows a shaded region for the confidence interval.

5. Will these two graphs look different? Why/why not?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

## `geom_smooth()` using method = 'loess'

ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess'

6. Recreate the R code necessary to generate the following graphs.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(size = 3) + 
  geom_smooth(se = FALSE, size = 1.5)

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, group = drv)) + 
  geom_point(size = 3) + 
  geom_smooth(se = FALSE, size = 1.5)

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point(size = 3) + 
  geom_smooth(se = FALSE, size = 1.5)

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(size = 3, mapping = aes(color = drv)) + 
  geom_smooth(se = FALSE, size = 1.5)

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(size = 3, mapping = aes(color = drv)) + 
  geom_smooth(se = FALSE, size = 1.5, mapping = aes(group = drv), 
                              linetype = "dashed", show.legend = TRUE)

## `geom_smooth()` using method = 'loess'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(size = 3, shape = 21, stroke = 1.2, mapping = aes(fill = drv), colour = "white")